But this example doesn't solve the problem I was thinking of: it shows lots of colors in the colorbar that aren't used in the plot.
&C On Mar 30, 2010, at 6:52 AM, Friedrich Romstedt wrote: > 2010/3/30 Ariel Rokem <aro...@berkeley.edu>: >> I ended up with the code below, using Chloe's previously posted >> 'subcolormap' and, in order to make the colorbar nicely attached to >> the main >> imshow plot, I use make_axes_locatable in order to generate the >> colorbar >> axes. I tried it out with a couple of use-cases and it seems to do >> what it >> is supposed to, (with ticks only for the edges of the range of the >> data and >> 0, if that is within that range), but I am not entirely sure. Do >> you think >> it works? > > I think even Chloe would agree that you should avoid the subcolormap() > if you can. I tried to create an as minimalistic as possible but > working self-contained example, please find the code also attached as > .py file: > > from matplotlib import pyplot as plt > import matplotlib as mpl > from mpl_toolkits.axes_grid import make_axes_locatable > import numpy as np > > fig = plt.figure() > ax_im = fig.add_subplot(1, 1, 1) > divider = make_axes_locatable(ax_im) > ax_cb = divider.new_vertical(size = '20%', pad = 0.2, pack_start = > True) > fig.add_axes(ax_cb) > > x = np.linspace(-5, 5, 101) > y = x > Z = np.sin(x*y[:,None]).clip(-1,1-0.1) > > # Leave out if you want: > Z += 2 > > min_val = Z.min() > max_val = Z.max() > bound = max(np.abs(Z.max()), np.abs(Z.min())) > > patch = ax_im.imshow(Z, origin = 'upper', interpolation = 'nearest', > vmin = -bound, vmax = bound) > > cb = fig.colorbar(patch, cax = ax_cb, orientation = 'horizontal', > norm = patch.norm, > boundaries = np.linspace(-bound, bound, 256), > ticks = [min_val, 0, max_val], > format = '%.2f') > > plt.show() > > Friedrich > <cbar.py><cbar.png> ------------------------------------------------------------------------------ Download Intel® Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
